Method and device for constructing formation pore pressure prediction model and storage medium

Abstract

The application discloses a method and device for constructing a formation pore pressure prediction model and a storage medium. The method comprises the following steps: obtaining a target layer pressure structure, core mineral components and pore structure characteristics of a carbonate rock; optimizing an Xu-Payne model according to the target layer pressure structure, the core mineral components and the pore structure characteristics to obtain an optimized Xu-Payne model; determining a bulk modulus of a carbonate rock matrix, a bulk modulus of a rock skeleton and an equivalent bulk modulus of a pore fluid based on the optimized Xu-Payne model; determining a bulk modulus of a saturated rock according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid; and constructing a formation pore pressure prediction model based on the bulk modulus of the saturated rock. The application can construct a formation pore pressure prediction model suitable for the carbonate rock, thereby improving the accuracy of predicting the formation pore pressure of the carbonate rock.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas field development, in particular to a method and device for constructing a formation pore pressure prediction model and a storage medium. BACKGROUND

[0002] Abnormal high pressure has an important influence on the development of conventional and unconventional oil and gas reservoirs, and can cause wellbore instability, drilling fluid loss, blowout and many other complex accidents. With the development of oil and gas reservoirs to complex and ultra-deep formations, formation pore pressure prediction plays an increasingly important role in the future development of oil and gas reservoirs. Reasonable and accurate prediction of formation pore pressure is the basis for reasonable design of drilling fluid density, design of well structure and maintenance of wellbore pressure, and is the guarantee of safe, effective and rapid drilling. Formation pore pressure prediction includes forward prediction with foresight, monitoring while drilling with timeliness and detection after drilling with reference, among which the detection after drilling has the highest accuracy, and the logging data of adjacent wells and exploration wells can provide theoretical support and reference for the detection of formation pore pressure of the target well.

[0003] The prior art has carried out a lot of research on the prediction and detection of formation pore pressure of clastic rocks, and has established various formation pore pressure prediction methods such as equivalent depth method, Eaton method and effective stress method, but all of them are empirical methods based on normal compaction and undercompaction theory. Carbonate rocks are different from clastic rocks. First, the diagenesis of carbonate rocks is complex, in addition to compaction, there are other effects such as cementation and dissolution, which leads to the fact that the formation pore pressure prediction method based on undercompaction is not applicable to carbonate rock formations. Second, the skeleton rigidity of carbonate rocks is large, and the heterogeneity is strong, so the corresponding parameters of logging and seismic cannot be responded, and the P-wave velocity and bulk compressibility coefficient do not have obvious change rules. Finally, the abnormal high pressure of carbonate rock formation is caused by strong epigenetic modification during deposition, which leads to the existence of multiple solutions of the response characteristics, so the conventional formation pore pressure prediction method suitable for clastic salt rock is difficult to apply to carbonate rock. Therefore, the existing formation pore pressure prediction method has the problem of being difficult to apply to carbonate rock. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a method and device for constructing a formation pore pressure prediction model and a storage medium, to solve the problem that the existing formation pore pressure prediction method is difficult to apply to carbonate rock.

[0005] In order to achieve the above-mentioned purpose, the first aspect of the present application provides a method for constructing a formation pore pressure prediction model, applied to a device for constructing a formation pore pressure prediction model, which comprises:

[0006] obtaining the target layer pressure structure, core mineral composition and pore structure characteristics of the carbonate rock;

[0007] According to the pressure structure of the target layer, the mineral composition of the core, and the pore structure characteristics, the Xu-Payne model is optimized to obtain an optimized Xu-Payne model;

[0008] Based on the optimized Xu-Payne model, the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid are determined respectively;

[0009] According to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid, the bulk modulus of the saturated rock is determined;

[0010] Based on the bulk modulus of the saturated rock, a formation pore pressure prediction model is constructed.

[0011] In the embodiments of the present application, based on the optimized Xu-Payne model, the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid are determined respectively, which includes:

[0012] Based on the optimized Xu-Payne model, the equivalent mineral composition of the carbonate rock matrix, the pore type, and the pore fluid are determined;

[0013] Based on the equivalent mineral composition, the bulk modulus of the carbonate rock matrix is determined;

[0014] Based on the pore type, the bulk modulus of the rock skeleton is determined;

[0015] Based on the corresponding bulk modulus and saturation of the pore fluid, the equivalent bulk modulus of the pore fluid is determined.

[0016] In the embodiments of the present application, based on the equivalent mineral composition, the bulk modulus of the carbonate rock matrix is determined, which includes:

[0017] According to the volume percentage and the bulk modulus corresponding to the equivalent mineral composition, the upper limit of the bulk modulus and the lower limit of the bulk modulus are determined respectively;

[0018] Combined with the upper limit of the bulk modulus and the lower limit of the bulk modulus, the bulk modulus of the carbonate rock matrix is determined through the VRH average theory.

[0019] In the embodiments of the present application, based on the corresponding bulk modulus and saturation of the pore fluid, the equivalent bulk modulus of the pore fluid is determined, which includes:

[0020] For the case of uniform filling of the pore fluid, through the Wood model, the first equivalent bulk modulus is determined according to the corresponding bulk modulus and saturation of the pore fluid;

[0021] For the case of separation of the pore fluid, through the Patchy model, the second equivalent bulk modulus is determined according to the corresponding bulk modulus and saturation of the pore fluid;

[0022] The first equivalent bulk modulus and the second equivalent bulk modulus are arithmetically averaged to determine the equivalent bulk modulus of the pore fluid.

[0023] In the embodiment of the present application, the bulk modulus of the rock matrix satisfies formula (1):

[0024]

[0025] wherein, K * is the bulk modulus of the rock matrix, φ is the porosity, N is the number of pore types, K i is the bulk modulus of the i-th pore type, V i is the volume fraction of the i-th pore type, P (*) (φ) is the geometric factor of the i-th pore type, which is determined by the aspect ratio of the pore type.

[0026] In the embodiment of the present application, the formation pore pressure prediction model is constructed based on the bulk modulus of the saturated rock, comprising:

[0027] determining the elastic modulus coupling parameter of the carbonate rock matrix, the rock matrix and the pore fluid;

[0028] determining the average value of the earth stress in multiple directions;

[0029] determining the volumetric strain of the saturated rock according to the average value of the earth stress in multiple directions and the bulk modulus of the saturated rock;

[0030] constructing the formation pore pressure prediction model according to the elastic modulus coupling parameter and the volumetric strain of the saturated rock.

[0031] In the embodiment of the present application, the elastic modulus coupling parameter of the carbonate rock matrix, the rock matrix and the pore fluid is determined, comprising:

[0032] determining the representation of the elastic properties of the pore fluid according to the bulk modulus of the carbonate rock matrix and the equivalent bulk modulus of the pore fluid;

[0033] determining the Biot coefficient according to the bulk modulus of the carbonate rock matrix and the bulk modulus of the rock matrix;

[0034] determining the elastic modulus coupling parameter according to the representation of the elastic properties of the pore fluid and the Biot coefficient.

[0035] In the embodiment of the present application, the formation pore pressure prediction model satisfies formula (2):

[0036]

[0037] wherein, P pP is the formation pore pressure, a is the Biot's coefficient, and M is a measure of the elastic properties of the pore fluid. K is the bulk modulus of the saturated rock.

[0038] The second aspect of the present application provides a device for constructing a formation pore pressure prediction model, comprising:

[0039] a memory configured to store instructions; and

[0040] a processor configured to call the instructions from the memory and capable of implementing the method for constructing a formation pore pressure prediction model as described above when executing the instructions.

[0041] The third aspect of the present application provides a machine-readable storage medium having instructions stored thereon for causing a machine to perform the method for constructing a formation pore pressure prediction model as described above.

[0042] Through the above technical solution, the pressure structure of the target layer system of the carbonate rock, the core mineral composition and the pore structure characteristics are obtained, and then the Xu-Payne model is optimized according to the pressure structure of the target layer system, the core mineral composition and the pore structure characteristics to obtain the optimized Xu-Payne model. Then, based on the optimized Xu-Payne model, the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid are determined, and then the bulk modulus of the saturated rock is determined according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid. Finally, the formation pore pressure prediction model is constructed based on the bulk modulus of the saturated rock. The present application can construct a formation pore pressure prediction model suitable for carbonate rocks based on the optimized Xu-Payne model, thereby improving the accuracy of predicting the formation pore pressure of carbonate rocks.

[0043] Other features and advantages of the present application will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings are included to provide a further understanding of the present application and constitute a part of the specification, which together with the specific embodiments below, serve to explain the present application, but do not constitute a limitation on the present application. In the drawings:

[0045] Figure 1 A flowchart of a method for constructing a formation pore pressure prediction model according to an embodiment of the present application is schematically shown;

[0046] Figure 2 A schematic diagram of a mineral composition according to an embodiment of the present application is schematically shown;

[0047] Figure 3 Fig. 1 schematically shows a diagram of formation pore pressure versus measured formation pressure according to an embodiment of the present application;

[0048] Figure 4 Fig. 2 schematically shows a flow chart of a method of building a formation pore pressure prediction model according to an embodiment of the present application;

[0049] Figure 5 Fig. 3 schematically shows a diagram of a target zone pressure structure according to an embodiment of the present application;

[0050] Figure 6 Fig. 4 schematically shows a structural block diagram of an apparatus of building a formation pore pressure prediction model according to an embodiment of the present application. DETAILED DESCRIPTION

[0051] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. It should be understood that the specific implementation manners described herein are merely used to explain and illustrate the embodiments of the present application, and should not be used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0052] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are merely used to explain the relative positional relationship, movement condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications also change accordingly.

[0053] In addition, if the embodiments of the present application involve descriptions such as "first", "second", etc., the descriptions of "first", "second", etc. are merely for description purposes, and should not be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features limited by "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of the various embodiments can be combined with each other, but it must be based on the fact that a person of ordinary skill in the art can realize it, and when the combination of technical solutions contradicts each other or cannot be realized, it should be considered that the combination of technical solutions does not exist, and is also not within the scope of protection claimed by the present application.

[0054] Figure 1 Fig. 1 schematically shows a diagram of formation pore pressure versus measured formation pressure according to an embodiment of the present application; Figure 1As shown, the embodiment of the present application provides a method for constructing a formation pore pressure prediction model, which is applied to a device for constructing a formation pore pressure prediction model. The method can include the following steps:

[0055] Step 101, obtaining the pressure structure of the target layer series of the carbonate rock, the mineral composition of the core, and the pore structure characteristics;

[0056] Step 102, optimizing the Xu-Payne model according to the pressure structure of the target layer series, the mineral composition of the core, and the pore structure characteristics to obtain an optimized Xu-Payne model;

[0057] Step 103, determining the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid based on the optimized Xu-Payne model;

[0058] Step 104, determining the bulk modulus of the saturated rock according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid;

[0059] Step 105, constructing a formation pore pressure prediction model based on the bulk modulus of the saturated rock.

[0060] In the following, the method for constructing a formation pore pressure prediction model provided by the embodiment of the present application will be described mainly taking a processor as an execution subject.

[0061] In the embodiment of the present application, the method for constructing a formation pore pressure prediction model is applied to a device for constructing a formation pore pressure prediction model, and can be used to predict the formation pore pressure of a carbonate rock reservoir. Through the measured data such as the formation pressure state of drill stem testing (DST) reaction and mud density, the processor can obtain the pressure structure of the target layer series of the carbonate rock. At the same time, the processor can determine the mineral composition of the core and the pore structure characteristics through X-ray diffraction (XRD) and scanning electron microscopy, respectively.

[0062] In the prior art, the Xu-Payne model equivalent carbonate rocks to a mixture of dolomite, calcite, dry clay, pore fluid and total porosity, however, the parameters of the Xu-Payne model are difficult to obtain, and the mineral composition and pore type of carbonate rocks are relatively complex, resulting in poor effect of the Xu-Payne model in actual application. To solve this problem, the embodiment of the present application optimizes the Xu-Payne model according to the pressure structure of the target layer system, the mineral composition of the core and the pore structure characteristics, to obtain an optimized Xu-Payne model. The optimized Xu-Payne model determines the equivalent mineral composition of the carbonate rock matrix as dolomite, calcite, anhydrite, quartz, halite and dry clay, determines the pore type as clay fracture, microfracture, intergranular pore and dissolution pore, and determines the pore fluid as water and gas. Figure 2 A schematic diagram of a mineral composition is schematically shown according to an embodiment of the present application. As shown in the figure, the mineral composition can include calcite, dolomite, anhydrite, quartz, halite, dry clay, potassium feldspar, pyrite, clay fracture, microfracture, intergranular pore and dissolution pore. Figure 2 As shown in the figure, the mineral composition can include calcite, dolomite, anhydrite, quartz, halite, dry clay, potassium feldspar, pyrite, clay fracture, microfracture, intergranular pore and dissolution pore.

[0063] Based on the optimized Xu-Payne model, the processor can determine the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock framework and the equivalent bulk modulus of the pore fluid, respectively, and further determine the bulk modulus of the saturated rock according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock framework and the equivalent bulk modulus of the pore fluid. After determining the bulk modulus of the saturated rock, the processor can determine the elastic modulus coupling parameter of the carbonate rock matrix, the rock framework and the pore fluid and the average of the stress in multiple directions, and determine the bulk strain of the saturated rock according to the average of the stress in multiple directions and the bulk modulus of the saturated rock. Finally, the processor can construct a formation pore pressure prediction model according to the elastic modulus coupling parameter and the bulk strain of the saturated rock.

[0064] By the technical solution, the pressure structure of the target layer system of the carbonate rock, the core mineral composition and the pore structure characteristics are obtained, and then the Xu-Payne model is optimized according to the pressure structure of the target layer system, the core mineral composition and the pore structure characteristics to obtain the optimized Xu-Payne model. Then, based on the optimized Xu-Payne model, the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid are determined respectively, and then the bulk modulus of the saturated rock is determined according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid. Finally, the formation pore pressure prediction model is constructed based on the bulk modulus of the saturated rock. Based on the optimized Xu-Payne model, the formation pore pressure prediction model suitable for the carbonate rock can be constructed, so that the accuracy of predicting the formation pore pressure of the carbonate rock is improved.

[0065] In the embodiment of the present application, step 103, based on the optimized Xu-Payne model, respectively determining the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid, can include:

[0066] Based on the optimized Xu-Payne model, the equivalent mineral composition, pore type and pore fluid of the carbonate rock matrix are determined.

[0067] Based on the equivalent mineral composition, the bulk modulus of the carbonate rock matrix is determined.

[0068] Based on the pore type, the bulk modulus of the rock skeleton is determined.

[0069] Based on the volume modulus and saturation degree corresponding to the pore fluid, the equivalent bulk modulus of the pore fluid is determined.

[0070] In the embodiment of the present application, the processor can determine the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid based on the optimized Xu-Payne model. The optimized Xu-Payne model determines the equivalent mineral composition of the carbonate rock matrix as dolomite, calcite, anhydrite, quartz, halite and dry clay, determines the pore type as clay fracture, microfracture, intergranular pore and dissolution pore hole, and determines the pore fluid as water and gas. Therefore, the processor can determine the equivalent mineral composition, pore type and pore fluid of the carbonate rock matrix. Further, the processor can determine the bulk modulus of the carbonate rock matrix based on the equivalent mineral composition, determine the bulk modulus of the rock skeleton based on the pore type, and determine the equivalent bulk modulus of the pore fluid based on the volume modulus and saturation degree corresponding to the pore fluid. In this way, the processor can determine the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid based on the optimized Xu-Payne model, which is convenient for subsequent construction of the formation pore pressure prediction model.

[0071] In the embodiments of the present application, the volume modulus of the carbonate rock matrix can be determined based on the equivalent mineral components, which can include:

[0072] The upper limit of the volume modulus and the lower limit of the volume modulus are respectively determined according to the volume percentage and the volume modulus corresponding to the equivalent mineral components;

[0073] The volume modulus of the carbonate rock matrix is determined by the VRH average theory in combination with the upper limit of the volume modulus and the lower limit of the volume modulus.

[0074] In the embodiments of the present application, the processor can determine the volume modulus of the carbonate rock matrix based on the equivalent mineral components. The processor can respectively determine the upper limit of the volume modulus and the lower limit of the volume modulus according to the volume percentage and the volume modulus corresponding to the equivalent mineral components. The upper limit of the volume modulus satisfies formula (3):

[0075]

[0076] The lower limit of the volume modulus satisfies formula (4):

[0077]

[0078] wherein, M V is the upper limit of the volume modulus, M R is the lower limit of the volume modulus, N i is the volume percentage of the i th mineral in the equivalent mineral components, K i is the volume modulus of the i th mineral, and H is the number of mineral types.

[0079] The physical parameters of the rock components at each depth in the carbonate rock formation can be determined according to the analysis of the wellhead returned cuttings and in combination with the logging data, or can be obtained by XRD analysis. Table 1 schematically shows the physical parameters of a rock component according to an embodiment of the present application. As shown in Table 1, the processor can determine the volume modulus corresponding to each mineral, and thus respectively determine the upper limit of the volume modulus and the lower limit of the volume modulus according to the volume percentage and the volume modulus corresponding to each mineral in the equivalent mineral components.

[0080] Table 1

[0081] Mineral or fluid Bulk modulus (GPa) Density (g / cm 3 )]]> Calcite 71.05 2.71 Dolomite 75.93 2.87 Anhydrite 66.09 2.98 Quartz 37.56 2.65 Halite 26.24 2.04 Dry clay 27.01 2.68 Potassium feldspar 48.02 2.56 Pyrite 158.08 4.99 Water 2.18 1.00 Gas 1.15 0.80

[0082] After the upper limit of the volume modulus and the lower limit of the volume modulus are determined, the processor can determine the volume modulus of the carbonate rock matrix by the VRH average theory. The volume modulus of the carbonate rock matrix satisfies formula (5):

[0083]

[0084] wherein K s is the bulk modulus of the carbonate rock matrix, N i is the volume percentage of the i th mineral in the equivalent mineral composition, K i is the bulk modulus of the i th mineral, and H is the number of mineral types.

[0085] In the embodiments of the present application, the bulk modulus of the rock matrix can satisfy formula (1):

[0086]

[0087] wherein K * is the bulk modulus of the rock matrix, φ is the porosity, N is the number of pore types, K i is the bulk modulus of the i th pore type, V i is the volume fraction of the i th pore type, P (*) is the geometric factor of the i th pore type, which is determined by the aspect ratio of the pore type.

[0088] In the embodiments of the present application, the processor can determine the bulk modulus of the rock matrix based on the pore types. The pore types can include clay cracks, micro cracks, intergranular pores and dissolution cavities. According to the porosity and the bulk modulus, volume fraction and geometric factor corresponding to the pore types, the processor can determine the bulk modulus of the rock matrix. In this way, the processor can determine the bulk modulus of the rock matrix based on the pore types.

[0089] In the embodiments of the present application, determining the equivalent bulk modulus of the pore fluid based on the bulk modulus and the saturation of the pore fluid can include:

[0090] For the case of uniform filling of the pore fluid, a first equivalent bulk modulus is determined according to the bulk modulus and the saturation of the pore fluid through the Wood model;

[0091] For the case of separation of the pore fluid, a second equivalent bulk modulus is determined according to the bulk modulus and the saturation of the pore fluid through the Patchy model;

[0092] The first equivalent bulk modulus and the second equivalent bulk modulus are arithmetically averaged to determine the equivalent bulk modulus of the pore fluid.

[0093] In the embodiment of the present application, the processor can determine the equivalent bulk modulus of the pore fluid based on the bulk modulus and the saturation of the pore fluid corresponding to the pore fluid. The filling mode of the pore fluid should be between the fully mixed case and the saturated patch filling case, therefore, the processor can determine a first equivalent bulk modulus according to the bulk modulus and the saturation of the pore fluid corresponding to the pore fluid through the Wood model for the case of uniform filling of the pore fluid. For the case of separation of the pore fluid, a second equivalent bulk modulus is determined according to the bulk modulus and the saturation of the pore fluid corresponding to the pore fluid through the Patchy model. The first equivalent bulk modulus satisfies formula (6):

[0094]

[0095] The second equivalent bulk modulus satisfies formula (7):

[0096] K f2 =S w K w +S g K g ; (7)

[0097] Wherein, K f1 is the first equivalent bulk modulus, K f2 is the second equivalent bulk modulus, S w is the saturation of water, S g is the saturation of gas, S w + g =1, K w is the bulk modulus of water, and K g is the bulk modulus of gas.

[0098] The processor can further perform arithmetic averaging on the first equivalent bulk modulus and the second equivalent bulk modulus to determine the equivalent bulk modulus of the pore fluid. The equivalent bulk modulus of the pore fluid satisfies formula (8):

[0099]

[0100] Wherein, K f is the equivalent bulk modulus of the pore fluid, K f1 is the first equivalent bulk modulus, and K f2 is the second equivalent bulk modulus. In this way, the processor can determine the equivalent bulk modulus of the pore fluid.

[0101] In the embodiment of the present application, step 105 of constructing a formation pore pressure prediction model based on the bulk modulus of the saturated rock can include:

[0102] Determining the elastic modulus coupling parameters of the carbonate rock matrix, the rock skeleton and the pore fluid;

[0103] determining an average value of the earth stress in multiple directions;

[0104] determining a volumetric strain of the saturated rock according to the average value of the earth stress in multiple directions and the bulk modulus of the saturated rock;

[0105] constructing a formation pore pressure prediction model according to the elastic modulus coupling parameter and the volumetric strain of the saturated rock.

[0106] In the embodiment of the present application, the processor can construct the formation pore pressure prediction model based on the bulk modulus of the saturated rock. First, the processor can determine the bulk modulus of the saturated rock according to the Biot-Gassmann equation. The bulk modulus of the saturated rock satisfies formula (9):

[0107] K = K * (φ) + α 2 M; (9)

[0108] wherein K is the bulk modulus of the saturated rock, K * (φ) is the bulk modulus of the rock skeleton, α is the Biot's coefficient, and M is a representation of the elastic property of the pore fluid.

[0109] The representation of the elastic property of the pore fluid satisfies formula (10):

[0110]

[0111] The Biot's coefficient satisfies formula (11):

[0112]

[0113] wherein M is a representation of the elastic property of the pore fluid, α is the Biot's coefficient, φ is the porosity, K s is the bulk modulus of the carbonate matrix, K * (φ) is the bulk modulus of the rock skeleton, and K f is the equivalent bulk modulus of the pore fluid.

[0114] Thus, the processor can determine the elastic modulus coupling parameter of the carbonate matrix, the rock skeleton, and the pore fluid. The elastic modulus coupling parameter satisfies formula (12):

[0115] A = αM; (12)

[0116] wherein A is the elastic modulus coupling parameter, α is the Biot's coefficient, and M is a representation of the elastic property of the pore fluid.

[0117] Further, the processor can determine an average value of the earth stress in multiple directions, so as to determine a volumetric strain of the saturated rock according to the average value of the earth stress in multiple directions and the bulk modulus of the saturated rock. The volumetric strain of the saturated rock satisfies formula (13):

[0118]

[0119] wherein, ε is the volumetric strain of the saturated rock, is the average of the earth stresses in multiple directions, K is the bulk modulus of the saturated rock.

[0120] Since the carbonate rock formation of the target layer system is relatively dense, it is difficult for the pore fluid in the overpressure system to be discharged, and it can be regarded as a closed system. The size of the formation overpressure is jointly determined by the deformation of the rock skeleton and the stress of the pore fluid. Therefore, the relationship between the formation overpressure and the external force acting on the formation is coupled by using the sleeve undrained model. The pore fluid is only subjected to the action of part of the earth stress, and the amount of the pore fluid does not change. The relationship between the formation pore pressure and the external stress can be expressed as formula (14):

[0121] P p =Aε; (14)

[0122] Combined with formula (12) and formula (13), the formation pore pressure prediction model satisfies formula (2):

[0123]

[0124] wherein, P p is the formation pore pressure, A is the elastic modulus coupling parameter, ε is the volumetric strain of the saturated rock, α is the Biot coefficient, M is the representation of the elastic properties of the pore fluid, is the average of the earth stresses in multiple directions, K is the bulk modulus of the saturated rock.

[0125] In the embodiments of the present application, the elastic modulus coupling parameter of the carbonate rock matrix, the rock skeleton and the pore fluid can include:

[0126] According to the bulk modulus of the carbonate rock matrix and the equivalent bulk modulus of the pore fluid, the representation of the elastic properties of the pore fluid is determined.

[0127] According to the bulk modulus of the carbonate rock matrix and the bulk modulus of the rock skeleton, the Biot coefficient is determined.

[0128] According to the representation of the elastic properties of the pore fluid and the Biot coefficient, the elastic modulus coupling parameter is determined.

[0129] In the embodiment of the present application, the processor can determine the representation of the elastic property of the pore fluid according to the Biot coefficient, the porosity, the bulk modulus of the carbonate matrix and the equivalent bulk modulus of the pore fluid. Meanwhile, the processor can determine the Biot coefficient according to the bulk modulus of the carbonate matrix and the bulk modulus of the rock skeleton. The processor can determine the elastic modulus coupling parameter by multiplying the representation of the elastic property of the pore fluid and the Biot coefficient. In this way, the processor can determine the elastic modulus coupling parameter so as to subsequently establish the formation pore pressure prediction model.

[0130] In the embodiment of the present application, the formation pore pressure prediction model can satisfy formula (2):

[0131]

[0132] wherein P is the formation pore pressure, a is the Biot coefficient, M is the representation of the elastic property of the pore fluid, p is the average of the earth stresses in multiple directions, and K is the bulk modulus of the saturated rock.

[0133] In the embodiment of the present application, the processor can construct the formation pore pressure prediction model. In actual application, by determining the Biot coefficient, the representation of the elastic property of the pore fluid, the average of the earth stresses in multiple directions and the bulk modulus of the saturated rock, the processor can realize the prediction of the formation pore pressure through the formation pore pressure prediction model. Figure 3 A schematic diagram of the comparison between the formation pore pressure and the measured formation pressure according to an embodiment of the present application is shown schematically. Figure 3 As shown in the embodiment of the present application, the prediction of the formation pore pressure can be realized through the formation pore pressure prediction model, and the accuracy of the predicted formation pore pressure is relatively high.

[0134] Figure 4 A flow chart of a method for constructing a formation pore pressure prediction model according to an embodiment of the present application is shown schematically. Figure 4 As shown in the embodiment of the present application, the method for constructing the formation pore pressure prediction model can include:

[0135] S1, analyzing the pressure structure of the target layer system;

[0136] S2, optimizing the Xu-Payne model based on the structure of the carbonate rock to obtain the optimized Xu-Payne model;

[0137] S3, determining the bulk modulus of the carbonate matrix according to the VRH average theory;

[0138] S4, determining the bulk modulus of the rock skeleton by using the DEM model; ​

[0139] S5. Determine the equivalent bulk modulus of the pore fluid based on its filling condition.

[0140] S6. Determine the bulk modulus of saturated rock according to the Biot-Gassmann equation;

[0141] S7. Establish a formation pore pressure prediction model.

[0142] In one specific embodiment of this application, the processor can analyze the target layer pressure structure. Figure 5 A schematic diagram of a target layered pressure structure according to a specific embodiment of this application is shown. Figure 5 As shown, the pressure structure of the Ordovician strata in a certain block of the Ordos Basin is described. The Ma-5 Member is dominated by normal pressure, weak overpressure, and strong overpressure, with a formation pressure coefficient ranging from 0.88 to 1.96. The overpressure amplitude varies considerably and does not show a clear correlation with depth. The Ma-4 Member exhibits normal pressure, weak overpressure, and strong overpressure, with a formation pressure coefficient ranging from 0.92 to 1.80. The vertical pressure distribution is also relatively complex, but the overpressure amplitude varies less than that of the Ma-5 Member. The Ma-3 Member exhibits weak overpressure to strong overpressure, with a formation pressure coefficient ranging from 1.46 to 1.81. The vertical pressure distribution is relatively concentrated.

[0143] Subsequently, the structure of the carbonate rock includes the target stratigraphic pressure structure, core mineral composition, and pore structure characteristics. The processor can optimize the Xu-Payne model based on the target stratigraphic pressure structure, core mineral composition, and pore structure characteristics to obtain an optimized Xu-Payne model. Based on the optimized Xu-Payne model, the processor can determine the bulk modulus of the carbonate matrix using VRH averaging theory, determine the bulk modulus of the rock skeleton using a DEM model, and determine the equivalent bulk modulus of pore fluids based on pore fluid filling conditions. Furthermore, combining the bulk modulus of the carbonate matrix, the rock skeleton, and the equivalent bulk modulus of pore fluids, the processor can determine the bulk modulus of saturated rock using the Biot-Gassmann equation, thereby establishing a formation pore pressure prediction model.

[0144] Figure 6 A schematic block diagram of an apparatus for constructing a formation pore pressure prediction model according to an embodiment of this application is shown. Figure 6 As shown in the figure, this application provides an apparatus for constructing a formation pore pressure prediction model, which may include:

[0145] Memory 610 is configured to store instructions; and

[0146] The processor 620 is configured to call instructions from the memory 610 and, when executing the instructions, to implement the aforementioned method for constructing a formation pore pressure prediction model.

[0147] Specifically, in the embodiment of the present application, the processor 620 can be configured to:

[0148] obtain the pressure structure of the target formation, the mineral composition of the core and the pore structure characteristics of the carbonate rock;

[0149] optimize the Xu-Payne model according to the pressure structure of the target formation, the mineral composition of the core and the pore structure characteristics of the carbonate rock to obtain an optimized Xu-Payne model;

[0150] determine the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid based on the optimized Xu-Payne model;

[0151] determine the bulk modulus of the saturated rock according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton and the equivalent bulk modulus of the pore fluid;

[0152] construct a formation pore pressure prediction model based on the bulk modulus of the saturated rock.

[0153] Further, the processor 620 can also be configured to:

[0154] determine the equivalent mineral composition, the pore type and the pore fluid of the carbonate rock matrix based on the optimized Xu-Payne model;

[0155] determine the bulk modulus of the carbonate rock matrix based on the equivalent mineral composition;

[0156] determine the bulk modulus of the rock skeleton based on the pore type;

[0157] determine the equivalent bulk modulus of the pore fluid based on the bulk modulus and the saturation degree corresponding to the pore fluid.

[0158] Further, the processor 620 can also be configured to:

[0159] determine the upper limit of the bulk modulus and the lower limit of the bulk modulus according to the volume percentage and the bulk modulus corresponding to the equivalent mineral composition, respectively;

[0160] determine the bulk modulus of the carbonate rock matrix by the VRH average theory in combination with the upper limit of the bulk modulus and the lower limit of the bulk modulus.

[0161] Further, the processor 620 can also be configured to:

[0162] for the case of uniform filling of the pore fluid, determine a first equivalent bulk modulus according to the bulk modulus and the saturation degree corresponding to the pore fluid by the Wood model;

[0163] For the case of pore fluid separation, the second equivalent bulk modulus is determined by the Patchy model according to the bulk modulus and the saturation of the pore fluid;

[0164] The first equivalent bulk modulus and the second equivalent bulk modulus are arithmetically averaged to determine the equivalent bulk modulus of the pore fluid.

[0165] In the embodiments of the present application, the bulk modulus of the rock matrix satisfies formula (1):

[0166]

[0167] wherein, K * is the bulk modulus of the rock matrix, φ is the porosity, N is the number of pore types, K i is the bulk modulus of the i-th pore type, V i is the volume fraction of the i-th pore type, P (*) (φ) is the geometric factor of the i-th pore type, which is determined by the aspect ratio of the pore type.

[0168] Further, the processor 620 can also be configured to:

[0169] determine the elastic modulus coupling parameter of the carbonate rock matrix, the rock matrix and the pore fluid;

[0170] determine the average value of the earth stress in multiple directions;

[0171] determine the volumetric strain of the saturated rock according to the average value of the earth stress in multiple directions and the bulk modulus of the saturated rock;

[0172] construct a formation pore pressure prediction model according to the elastic modulus coupling parameter and the volumetric strain of the saturated rock.

[0173] Further, the processor 620 can also be configured to:

[0174] determine the representation of the elastic properties of the pore fluid according to the bulk modulus of the carbonate rock matrix and the equivalent bulk modulus of the pore fluid;

[0175] determine the Biot coefficient according to the bulk modulus of the carbonate rock matrix and the bulk modulus of the rock matrix;

[0176] determine the elastic modulus coupling parameter according to the representation of the elastic properties of the pore fluid and the Biot coefficient.

[0177] In the embodiments of the present application, the formation pore pressure prediction model satisfies formula (2):

[0178]

[0179] wherein, P pis the formation pore pressure, a is the Biot's coefficient, and M is a measure of the elastic properties of the pore fluid, is the average of the stresses in multiple directions, and K is the bulk modulus of the saturated rock.

[0180] By the technical solution, the pressure structure of the target layer system of the carbonate rock, the core mineral composition, and the pore structure characteristics are obtained, and then the Xu-Payne model is optimized according to the pressure structure of the target layer system, the core mineral composition, and the pore structure characteristics, to obtain the optimized Xu-Payne model. Then, the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid are determined based on the optimized Xu-Payne model, and then the bulk modulus of the saturated rock is determined according to the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid. Finally, the formation pore pressure prediction model is constructed based on the bulk modulus of the saturated rock. Based on the optimized Xu-Payne model, the formation pore pressure prediction model suitable for the carbonate rock can be constructed, so that the accuracy of predicting the formation pore pressure of the carbonate rock is improved.

[0181] The embodiment of the present application also provides a machine readable storage medium, which stores instructions for causing a machine to execute the method for constructing the formation pore pressure prediction model.

[0182] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0183] The present application is described with reference to flowcharts and / or block diagrams according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The function of one flow or multiple flows and / or blocks Figure 1 The function of one flow or multiple flows and / or blocks

[0184] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0185] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that are executed on the computer or other programmable apparatus provide steps for implementing the Figure 1 function specified in the flow or flows and / or blocks Figure 1 of the block or blocks.

[0186] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0187] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. The memory can also include non-volatile memory, such as read only memory (ROM), electrically programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, or non-volatile random access memory (NVRAM), for the storage of information, such as data files or program

[0188] Computer readable media includes permanent and non-permanent, removable and non-removable media implemented in any method or technology for the storage of information. The information can be computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile discs (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer readable media does not include transitory media, such as modulated data signals and carrier waves.

[0189] It should also be noted that the terms "comprising", "comprises" or other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0190] The above embodiments are only used to illustrate the present application, but not to limit it. Instead of the above, various modifications and changes can be made to the application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application shall fall into the scope of the claims of the application.

Claims

1. A method for constructing a formation pore pressure prediction model, characterized in that, The method includes: To obtain the target stratigraphic pressure structure, core mineral composition, and pore structure characteristics of carbonate rocks; The Xu-Payne model was optimized based on the target stratigraphic pressure structure, the core mineral composition, and the pore structure characteristics to obtain the optimized Xu-Payne model. Based on the optimized Xu-Payne model, the bulk modulus of the carbonate matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid were determined respectively. The bulk modulus of the saturated rock is determined based on the bulk modulus of the carbonate rock matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of the pore fluid. A formation pore pressure prediction model is constructed based on the bulk modulus of the saturated rock. The determination of the bulk modulus of the carbonate matrix, the bulk modulus of the rock skeleton, and the equivalent bulk modulus of pore fluids based on the optimized Xu-Payne model includes: Based on the optimized Xu-Payne model, the equivalent mineral composition, pore type, and pore fluid of the carbonate matrix are determined. The bulk modulus of the carbonate rock matrix is ​​determined based on the equivalent mineral composition. The bulk modulus of the rock skeleton is determined based on the pore type. The equivalent bulk modulus of the pore fluid is determined based on the bulk modulus and saturation of the pore fluid. The determination of the bulk modulus of the carbonate rock matrix based on the equivalent mineral composition includes: The upper limit and lower limit of the bulk modulus are determined based on the volume percentage and bulk modulus corresponding to the equivalent mineral components, respectively. The bulk modulus of the carbonate rock matrix is ​​determined by combining the upper and lower limits of the bulk modulus using the VRH averaging theory.

2. The method according to claim 1, characterized in that, Determining the equivalent bulk modulus of the pore fluid based on its corresponding bulk modulus and saturation includes: For the case where the pore fluid is uniformly filled, the first equivalent bulk modulus is determined using the Wood model based on the corresponding bulk modulus and saturation of the pore fluid. For the case of pore fluid separation, the second equivalent bulk modulus is determined by using the Patchy model based on the bulk modulus and saturation of the pore fluid. The equivalent bulk modulus of the pore fluid is determined by taking the arithmetic mean of the first and second equivalent bulk moduli.

3. The method according to claim 1, characterized in that, The bulk modulus of the rock skeleton satisfies formula (1): ； （1） in, Let be the bulk modulus of the rock skeleton. Porosity The number of pore types, For the first i Bulk modulus of various pore types For the first i Volume fraction of different pore types For the first i The geometric factor of a pore type is determined by the aspect ratio of the pore type.

4. The method according to claim 1, characterized in that, The formation pore pressure prediction model constructed based on the bulk modulus of the saturated rock includes: Determine the coupling parameters of elastic modulus of carbonate matrix, rock skeleton and pore fluid; Determine the average value of the geostress in multiple directions; The volumetric strain of the saturated rock is determined based on the average value of the geostress in the multiple directions and the bulk modulus of the saturated rock. A formation pore pressure prediction model is constructed based on the elastic modulus coupling parameter and the volumetric strain of the saturated rock.

5. The method according to claim 4, characterized in that, The parameters for determining the elastic modulus coupling of carbonate rock matrix, rock skeleton, and pore fluid include: Characteristic quantities for the elastic properties of pore fluids are determined based on the bulk modulus of the carbonate rock matrix and the equivalent bulk modulus of the pore fluids. The Biot coefficient is determined based on the bulk modulus of the carbonate rock matrix and the bulk modulus of the rock skeleton. The elastic modulus coupling parameter is determined based on the characterization of the pore fluid elastic properties and the Biot coefficient.

6. The method according to claim 5, characterized in that, The formation pore pressure prediction model satisfies formula (2): ；（2） in, For formation pore pressure, The Biot coefficient is mentioned. This is a characterizing quantity for the elastic properties of the pore fluid. This represents the average value of the geostress in the multiple directions. The bulk modulus of the saturated rock is given.

7. An apparatus for constructing a formation pore pressure prediction model, characterized in that, include: The memory is configured to store instructions; as well as A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for constructing a formation pore pressure prediction model according to any one of claims 1 to 6.

8. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform a method for constructing a formation pore pressure prediction model according to any one of claims 1 to 6.

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